US-12627949-B2 - Selective triggering of neural network functions for positioning of a user equipment

Abstract

In an aspect, a UE obtains information (e.g., UE-specific information, etc.) associated with a set of triggering criteria for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedure, obtains positioning measurement data associated with a location of the UE, and determines a positioning estimate for the UE based at least in part upon the positioning measurement data and at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria.

Inventors

• Jay Kumar Sundararajan
• Krishna Kiran Mukkavilli
• Taesang Yoo
• Naga Bhushan
• June Namgoong
• Pavan Kumar Vitthaladevuni
• Tingfang JI

Assignees

• QUALCOMM INCORPORATED

Dates

Publication Date

20260512

Application Date

20240703

Claims (20)

1. 1 . A method of operating a user equipment (UE), comprising: obtaining, by the UE, information associated with a set of triggering criteria for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedures; obtaining, by the UE, positioning measurement data associated with a location of the UE; and determining, by the UE, a positioning estimate for the UE based at least in part upon the positioning measurement data and at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria, wherein the UE is a subscriber device that subscribes to one or more communications services provided via a communications network.
2. 2 . The method of claim 1 , wherein the set of triggering criteria is received at the UE from a serving network or an external server.
3. 3 . The method of claim 1 , further comprising: receiving a plurality of neural network functions from the set of neural network functions; and selecting the at least one neural network function from among the plurality of neural network functions based on the at least one triggering criterion from the set of triggering criteria.
4. 4 . The method of claim 1 , further comprising: receiving, from a network component, a query for current information associated with the UE; transmitting, to the network component in response to the query, the obtained information; and receiving, from the network component in response to the transmission of the obtained information, an indication of the at least one neural network function based on the obtained information satisfying the at least one triggering criterion.
5. 5 . The method of claim 4 , wherein the indication comprises the at least one neural network function or a reference to the at least one neural network function.
6. 6 . The method of claim 4 , wherein the query is received at the UE responsive to a handoff of the UE, or wherein the indication is received at the UE responsive to the handoff of the UE, or a combination thereof.
7. 7 . The method of claim 1 , wherein the set of neural network functions is aggregated into a single neural network function construct.
8. 8 . The method of claim 7 , wherein the obtained information is provided as a set of inputs into the single neural network function construct, and wherein the determining comprises execution of the single neural network function construct based on the set of inputs.
9. 9 . The method of claim 1 , wherein the at least one neural network function comprises a UE-feature processing neural network function, at least one wireless network component-feature processing neural network function, or a combination thereof.
10. 10 . The method of claim 1 , wherein the obtained information comprises one or more of geographic region characteristics of the UE, whether the UE is located in an indoor or outdoor environment, a serving wireless network component or carrier network of the UE, a UE category, a wireless network component category, or any combination thereof.
11. 11 . The method of claim 1 , wherein the set of triggering criteria is associated with one or more of geographic region characteristics, an indoor or outdoor UE status, a wireless network component or carrier network, a UE category, a wireless network component category, or any combination thereof.
12. 12 . A user equipment (UE), comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to: obtain information associated with a set of triggering criteria for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedures; obtain positioning measurement data associated with a location of the UE; and determine a positioning estimate for the UE based at least in part upon the positioning measurement data and at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria, wherein the UE is a subscriber device that subscribes to one or more communications services provided via a communications network.
13. 13 . The UE of claim 12 , wherein the set of triggering criteria is received at the UE from a serving network or an external server.
14. 14 . The UE of claim 12 , wherein the at least one processor is further configured to: receive, via the at least one transceiver, a plurality of neural network functions from the set of neural network functions; and select the at least one neural network function from among the plurality of neural network functions based on the at least one triggering criterion from the set of triggering criteria.
15. 15 . The UE of claim 12 , wherein the at least one processor is further configured to: receive, via the at least one transceiver, from a network component, a query for current information associated with the UE; transmit, via the at least one transceiver, to the network component in response to the query, the obtained information; and receive, via the at least one transceiver, from the network component in response to the transmission of the obtained information, an indication of the at least one neural network function based on the obtained information satisfying the at least one triggering criterion.
16. 16 . The UE of claim 15 , wherein the indication comprises the at least one neural network function or a reference to the at least one neural network function.
17. 17 . The UE of claim 15 , wherein the query is received at the UE responsive to a handoff of the UE, or wherein the indication is received at the UE responsive to the handoff of the UE, or a combination thereof.
18. 18 . The UE of claim 12 , wherein the set of neural network functions is aggregated into a single neural network function construct.
19. 19 . The UE of claim 18 , wherein the obtained information is provided as a set of inputs into the single neural network function construct, and wherein the determining comprises execution of the single neural network function construct based on the set of inputs.
20. 20 . The UE of claim 12 , wherein the at least one neural network function comprises a UE-feature processing neural network function, at least one wireless network component-feature processing neural network function, or a combination thereof.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS The present Application for Patent is a Continuation of U.S. Non-Provisional application Ser. No. 17/391,594, entitled “SELECTIVE TRIGGERING OF NEURAL NETWORK FUNCTIONS FOR POSITIONING OF A USER EQUIPMENT,” filed Aug. 2, 2021, which in turn claims the benefit of U.S. Provisional Application No. 63/061,064, entitled “SELECTIVE TRIGGERING OF NEURAL NETWORK FUNCTIONS FOR POSITIONING OF A USER EQUIPMENT,” filed Aug. 4, 2020, each of which is assigned to the assignee hereof and expressly incorporated herein by reference in its entirety. BACKGROUND OF THE DISCLOSURE 1. Field of the Disclosure Aspects of the disclosure relate generally to wireless communications, and more particularly to selective triggering of neural network functions for positioning of a user equipment (UE). 2. Description of the Related Art Wireless communication systems have developed through various generations, including a first-generation analog wireless phone service (1G), a second-generation (2G) digital wireless phone service (including interim 2.5G networks), a third-generation (3G) high speed data, Internet-capable wireless service and a fourth-generation (4G) service (e.g., LTE or WiMax). There are presently many different types of wireless communication systems in use, including cellular and personal communications service (PCS) systems. Examples of known cellular systems include the cellular analog advanced mobile phone system (AMPS), and digital cellular systems based on code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), the Global System for Mobile access (GSM) variation of TDMA, etc. A fifth generation (5G) wireless standard, referred to as New Radio (NR), enables higher data transfer speeds, greater numbers of connections, and better coverage, among other improvements. The 5G standard, according to the Next Generation Mobile Networks Alliance, is designed to provide data rates of several tens of megabits per second to each of tens of thousands of users, with 1 gigabit per second to tens of workers on an office floor. Several hundreds of thousands of simultaneous connections should be supported in order to support large wireless sensor deployments. Consequently, the spectral efficiency of 5G mobile communications should be significantly enhanced compared to the current 4G standard. Furthermore, signaling efficiencies should be enhanced and latency should be substantially reduced compared to current standards. SUMMARY The following presents a simplified summary relating to one or more aspects disclosed herein. Thus, the following summary should not be considered an extensive overview relating to all contemplated aspects, nor should the following summary be considered to identify key or critical elements relating to all contemplated aspects or to delineate the scope associated with any particular aspect. Accordingly, the following summary has the sole purpose to present certain concepts relating to one or more aspects relating to the mechanisms disclosed herein in a simplified form to precede the detailed description presented below. In an aspect, a method of operating a user equipment (UE) includes obtaining information associated with a set of triggering criteria for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedures; obtaining positioning measurement data associated with a location of the UE; and determining a positioning estimate for the UE based at least in part upon the positioning measurement data and at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria. In some aspects, the set of triggering criteria is received at the UE from a serving network or an external server. In some aspects, the method includes receiving a plurality of neural network functions from the set of neural network functions; and selecting the at least one neural network function from among the plurality of neural network functions based on the at least one triggering criterion from the set of triggering criteria. In some aspects, the method includes receiving, from a network component, a query for current information associated with the UE; transmitting, to the network component in response to the query, the obtained information; and receiving, from the network component in response to the transmission of the obtained information, an indication of the at least one neural network function based on the obtained information satisfying the at least one triggering criterion. In some aspects, the indication comprises the at least one neural network function or a reference to the at least one neu